You didn’t think you could predict the future, until now

We all know that vast swathes of information – data – are being stored on computers all over the world – the cloud. But what will we do with all this data? What are the benefits for our customers? For our companies?

This is where we can see Artificial Intelligence – machine learning – stepping in to take all that big data and turn it into something that can achieve something almost magical for the modern marketer: predicting the future.

In this post we’ll see some example of how big data and machine learning are predicting the future, and lessons you can learn to apply to your marketing, today.

Predicting the World Cup Winners

Only last month when Microsoft’s Bing blew away the bookies, by predicting nearly every winner of every match in the 2014 World Cup in Brazil.

Microsoft Bing’s prediction technology put out a solid final record of 15 out of 16, with the only wrong prediction being Brazil over the Netherlands for 3rd place.

Using algorithms and data (that included a team’s margin of victory in past matches, team records, home advantage and the influence of weather and other outside factors) Bing was able to predict winners based on hard data rather than human bias or ‘expert’ opinions.

If computer programs can predict World Cup winners with pinpoint precision, what else can it predict?

Although it sounds like science fiction, computers have the potential to choose movies for us to personalising medication based on our genomes. It’s being referred to as the “new Artificial Intelligence,” and it can seem akin to mind reading.

Big Data is already making recommendations on our interests in entertainment based on our library, viewing and listening habits.

Tailored as choices based on recent browsing history is simple enough that it’s been implemented for years, and Netflix takes that concept a step further.

When Truth Becomes Fiction

In 2013, the streaming media company launched three separate original shows, each of which have made a splash. The political thriller “House of Cards” was Netflix’s first foray into original programming, and it was brought about by Big Data.

By noting the popularity of certain types of programs, as well as directors and actors, Netflix was able to come up with a formula for tailor-made shows that would appeal to a vast audience. The service also reportedly personalised the show’s trailers to the individual viewer; for instance, female viewers were shown trailers that focused heavily on the show’s strong female leads.

It was formula that was highly fruitful. “House of Cards” was nominated for multiple Emmy awards, and their third effort, “Orange is the New Black,” made a similar impact on viewers. Although specific viewing numbers aren’t easy to determine, the series picked up 12 Emmy nods in 2014.

That Netflix can successfully predict what we want shouldn’t come as a surprise – our browsing history has been subject to analysis for years now.

When Computers Get Personal

IBM Watson, which you may remember as the computer that in 2011 beat humans while playing the US hit game show Jeopardy, is now being employed in the healthcare industry. By inputting a patient’s genome sequencing and specific tumour into Watson, specific treatment plans and the medications that will make the biggest impact on a tumour can be determined.

The biggest lesson we can get from seeing Watson in action is that computer programs eliminate personal bias, and even flawed popular opinion, altogether. The massive amounts of data collected by the hospital can be overwhelming for human researchers as well as costly. Studies suggest that the cost of analysing a single genome is close to $17,000, and by eliminating the need for human data interpretation Watson can greatly reduce that number.

IBM’s Watson will “improve results worldwide”. According to staff at the Memorial Sloan Kettering Cancer Center, and it will reduce the associated costs.

In short, Watson means a win-win situation for healthcare facilities; evidence-based medicine is tailored to the patient, and Watson learns from healthcare professionals, meaning that the program and human interaction form a single, cohesive package.

When Your Gut Is Wrong

Watson’s removal of human prejudices is literally being applied to life or death situations. But we all face decisions in our work where instinct may be flawed, basing decisions on the wrong signals – signals that are actually noise.

As made famous by Brad Pitt’s Oscar winning 2011 movie Moneyball (scripted by Aaron Sorkin), the Oakland A’s baseball team – faced with a small salary budget going in to the 2002 season – used data analysis to upend traditional baseball wisdom.

Often talent scouts would base decisions on the ‘look’ of the player, and other vanity metrics. But the A’s found some player qualities that were undervalued in the market (such as slugging percentage and on-base percentage) are solid indicators of offensive success. So they chose players with those previously undervalued attributes, which led to the team reaching the playoffs in 2002 and 2003, spending far less money in the process.

Today everyone has reduced budgets, so these are lessons every modern marketer should learn from. Are there aspects in your work life on which you base decisions on noise rather than signal?

Marketing That Sees The Future

Like Watson, how do we establish the “DNA” of our customers – in order to predict their tastes, and better serve their needs? By applying the same logic as IBM’s Watson, Microsoft’s Bing, and indeed The Oakland A’s baseball team, modern marketers will not research historic activity, they will predict future activity.

Imagine how trends can be predicted by mapping social sentiment from cultural influencers, from those that have been proven to shape tastes? Malcolm Gladwell’s book ‘The Tipping Point’ famously showed how New York hipsters sparked a craze for Hush Puppy shoes in the mid 90’s.

At the fingertips of agile marketers – and with rapid product prototyping – new concepts for brands or brand extensions could be piloted in these focused markets. With lean development plans, these could be iterated and rapidly scaled based on performance data.